Abstract: Image Inpainting is the art of filling in missing data in an image. The purpose of inpainting is to reconstruct missing regions in a visually plausible manner so that it seems reasonable to the human eye. There have been several approaches proposed for the same. In this paper, we present an algorithm that improves and extends a previously proposed algorithm and provides faster inpainting. Using our approach, one can inpaint large regions (e.g. remove an object etc.) as well as recover small portions (e.g. restore a photograph by removing cracks etc.). The inpainting is based on the exemplar based approach. The basic idea behind this approach is to find examples (i.e. patches) from the image and replace the lost data with it. We obtained good quality results quickly using our approach.Image Inpainting is the process of reconstructing lost or deteriorated part of images based on thebackground information.This paper introduces a novel framework for exemplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method.
Keywords: Image inpainting, super resolution inpainting, Low-resolution, High Resolution, exemplar-based inpainting.